Dynamic bandwidth allocation and service differentiation for broadband passive optical networks

ABSTRACT

A dynamic upstream bandwidth allocation scheme is disclosed, i.e., limited sharing with traffic prediction (LSTP), to improve the bandwidth efficiency of upstream transmission over PONs. LSTP adopts the PON MAC control messages, and dynamically allocates bandwidth according to the on-line traffic load. The ONU bandwidth requirement includes the already buffered data and a prediction of the incoming data, thus reducing the frame delay and alleviating the data loss. ONUs are served by the OLT in a fixed order in LSTP to facilitate the traffic prediction. Each optical network unit (ONU) classifies its local traffic into three classes with descending priorities: expedited forwarding (EF), assured forwarding (AF), and best effort (BE). Data with higher priority replace data with lower priority when the buffer is full. In order to alleviate uncontrolled delay and unfair drop of the lower priority data, the priority-based scheduling is employed to deliver the buffered data in a particular transmission timeslot. The bandwidth allocation incorporates the service level agreements (SLAs) and the on-line traffic dynamics. The basic limited sharing with traffic prediction (LSTP) scheme is extended to serve the classified network traffic.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. ProvisionalApplication No. 60/671,973, filed Apr. 15, 2005, entitled LIMITEDSHARING WITH TRAFFIC PREDICTION FOR DYNAMIC BANDWIDTH ALLOCATION OVERBROADBAND PASSIVE OPTICAL NETWORKS AND SERVICE DIFFERENTIATIONENHANCEMENT THEREFOR.

BACKGROUND OF THE INVENTION

This invention relates to the problem of dynamic bandwidth allocationover passive optical networks. It arbitrates the upstream channelbandwidth among multiple optical network units (ONUs). In addition, thisinvention relates to the problem of service differentiation over passiveoptical networks (PONs). It integrates queuing, scheduling, andclass-based bandwidth allocation to serve diverse end users.Specifically, the basic limited sharing with traffic prediction (LSTP)scheme is extended to serve the classified network traffic overbroadband PONs (EPONs, BPONs, GPONs).

Passive optical networks (PONs) address the first mile of thecommunication infrastructure between the service provider centraloffices and the customer sites, also known as the “access network.” Withthe expansion of services offered over the Internet, a dramatic increaseof bandwidth has been facilitated in the backbone network through theuse of wavelength division multiplexing (WDM), providing tens ofGigabits per second per wavelength. At the same time, the local areanetworks (LANs) have been scaled up from 10 Mbps to 100 Mbps and arebeing upgraded to Gigabit Ethernets. Such a growing gap between thecapacity of the backbone network and the end users' needs results in theserious bottleneck of the access network in between [3]. It is desiredto have access network technology that can provide low cost andefficient equipment to facilitate multi-service access to the end users.PONs are considered as an attractive and promising solution to thebroadband subscriber access network. As an inexpensive, simple, andscalable technology, and with the capability of delivering integratedservices, PONs are deliberated in the standardization process of theIEEE 802.3ah Ethernet in the First Mile (EFM) Task Force [1] and ITU-TStudy Group 15 [2], which aim to significantly increase the broadbandservice performance while minimizing equipment, operation, andmaintenance costs.

As a low-cost, high-speed technology, and with the recent approval ofPON standards IEEE 802.3ah, ITU-T G.983x, and ITU-T G.984x, PONs are anattractive and promising solution to the broadband subscriber accessnetwork. As illustrated in FIG. 1, a PON consists of an optical lineterminal (OLT) located at the provider central office and a set ofassociated optical network units (ONUs) that deliver broadband servicesto the end users. A single fiber extends from an OLT to a 1:N passiveoptical splitter, which fans out multiple single fiber drops todifferent ONUs. The active electronic components in the traditionalaccess networks, such as regenerators and amplifiers, are eliminated inPONs and replaced with the less expensive passive optical splitters,which are simpler and easier to maintain. A major feature of PONs is theutility of a shared upstream channel among multiple ONUs, and thusbandwidth management is a critical issue in order to improve the PONefficiency. The existing bandwidth allocation schemes pose some criticallimitations. One of the major problems is that the upstream dataarriving during the waiting time cannot be delivered in the nexttimeslot, thus posing additional data delay, severe data loss, andlonger queue size. These are the barriers to achieving high bandwidthefficiency over broadband access networks. As a result, known availablebandwidth allocation schemes are inefficient over these networks.

Since the access network is required to accommodate various kinds oftraffic, service differentiation is a distinguished feature that PONsare expected to provide. Owing to the differences in subscriber'sservice level agreements (SLAs), different end users may have differentbandwidth requirements. A pragmatic approach is to employ timeslot-basedbandwidth allocation by providing various lengths of timeslots to servedifferent traffic. The existing service differentiation schemes posesome critical limitations. The major problems include how to enqueue andschedule the local traffic, and how to allocate the upstream bandwidthto different queues. Available service differentiation schemes onlytackle part of the problem, and they are inefficient for deliveringdiverse traffic over PONs.

Data are broadcasted from the OLT downstream to the ONUs using theentire bandwidth of the downstream channel. ONUs selectively receivedata destined for them by matching the carried destination addresses.

The process of transporting data upstream to the OLT over PONs isdifferent from that of transporting data downstream to the local users.In the upstream direction, a different channel wavelength is employedfor the upstream traffic, and multiple ONUs share this common upstreamchannel. Therefore, only a single ONU may transmit during a timeslot inorder to avoid data collisions. Data from local users are first bufferedat an ONU until the exclusively assigned timeslot arrives. The buffereddata would be “bursted” out to the OLT in the timeslot at the fullchannel speed.

In order to provide diverse quality of service (QoS), the bandwidthmanagement of the upstream channel is a critical issue for thesuccessful implementation of PONs. Different PON technologies have theirown MAC control messages to facilitate the upstream bandwidthallocation. For example, EPONs adopt MultiPoint Control Protocol (MPCP)[1] developed by the IEEE 802.3ah EFM Task Force. The REPORT message isused by the ONU to report the bandwidth requirement to the OLT, whilethe GATE message is used by the OLT to assign the timeslot for aspecific ONU. There have been numerous proposals in the literature totackle the upstream bandwidth allocation.

The limited bandwidth allocation (LBA) [3] scheme grants an ONU therequested timeslot length, but no more than an upper bound. Thebandwidth guaranteed polling (BGP) scheme [4] allocates the timeslot toan ONU according to its service level agreement (SLA). Choi and Huh [5]proposed the classified bandwidth allocation for multimedia services.However, the BGP scheme is incompatible with the PON standard and wouldnot be standardized. The LBA scheme and the Choi and Huh scheme do notconsider the incoming data during the ONU waiting time, which rangesfrom sending the queue length report to sending the buffered data, andthus, such data have to be deferred to the next timeslot, posingadditional delay and loss.

Assi et al. [6] predicted such incoming data of the high prioritytraffic in a rough way by simply replacing it with the actual number ofincoming data during the last waiting time. The drawback is that theservice order of ONUs changes drastically, with the heavily loaded ONUsalways being served after the lightly loaded ones, and therefore, theprediction of the incoming high priority traffic is severely impairedbecause the waiting time of each ONU may change drastically.

SUMMARY OF THE INVENTION

The method of the present invention provides a technique for allocatingthe upstream channel bandwidth efficiently and dynamically amongmultiple ONUs with a very low time and space complexity. The presentmethod also provides a technique for differentiating the service todiverse network traffic through a combination of queuing, scheduling,and class-based bandwidth allocation.

We disclose a dynamic upstream bandwidth allocation scheme, i.e.,limited sharing with traffic prediction (LSTP), to improve the bandwidthefficiency of upstream transmission over PONs. LSTP adopts the standardMAC control messages, and dynamically allocates bandwidth according tothe on-line traffic load. The ONU report includes the already buffereddata and a prediction of the incoming data, thus reducing the data delayand alleviating the data loss. ONUs are served by the OLT in a fixedorder in LSTP to facilitate the traffic prediction.

In order to improve the bandwidth efficiency over PONs, the upstreambandwidth is dynamically allocated according to the on-line networktraffic load. At the end of each timeslot, an ONU reports its localqueue status, including the already buffered data and a prediction ofthe data arriving during the waiting time. The prediction process isbased on the bursty nature of network traffic, and has a very lowcomputational complexity that makes the disclosed scheme scalable forany size of PONs. The traffic prediction contributes to the reduction ofdata latency and data loss.

With respect to service differentiation, each optical network unit (ONU)classifies its local traffic into three classes with descendingpriorities: expedited forwarding (EF), assured forwarding (AF), and besteffort (BE). Data with higher priority replace data with lower prioritywhen the buffer is full. In order to alleviate uncontrolled delay andunfair drop of the lower priority data, the priority-based scheduling isemployed to deliver the buffered data in a particular transmissiontimeslot. The bandwidth allocation incorporates the service levelagreements (SLAs) and the on-line traffic dynamics. The basic limitedsharing with traffic prediction (LSTP) scheme is extended to serve theclassified network traffic.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing brief description, as well as further objects features andadvantages will be understood more completely from the followingdetailed description of presently preferred, but nonethelessillustrative, embodiments in accordance with the present invention, withreference being had to the accompanying drawings, wherein:

FIG. 1 is a functional block diagram of a passive optical network (PON);

FIG. 2 is a representation of a REPORT message and a GATE message asused in accordance with a preferred embodiment of the present invention;

FIG. 3 is a timing chart useful in explaining the operation of an EPONscenario;

FIG. 4 is a timing chart useful in explaining the operation of an EPONincorporating limited sharing with traffic prediction (LSTP) for dynamicbandwidth allocation over EPONs;

FIG. 5 is a graph of average frame delay vs. network traffic load forthe type of system represented by FIG. 4; and

FIG. 6 is a graph of from loss ration vs. network traffic load for thetype of system represented by FIG. 4.

DETAILED DESCRIPTION OF THE PREFERRRED EMBODIMENTS

The major challenges of PONs include the MAC protocol design and themulti-service provisioning. Because of the directional property of thepassive optical splitter, it is difficult for an ONU to detect datacollisions by the conventional CSMA/CD MAC protocol which was designedfor Ethernet. Therefore, an efficient MAC protocol is crucial to ensurehigh bandwidth utilization. Since PONs do not support QoS (quality ofservice) directly while the access network is required to accommodatevarious kinds of traffic, multi-service access is a distinguishedfeature that PONs are expected to provide. Owing to the differences insubscriber's service level agreements (SLAs), different ONUs may havedifferent bandwidth requirements. A pragmatic approach is to employ thetimeslot-based bandwidth allocation by providing various lengths oftimeslots to different ONUs.

Fixed bandwidth allocation (FBA) grants each ONU a fixed timeslot lengthin every service cycle. A service cycle is defined as the time that eachONU transmits its data once to the OLT. FBA works exactly like the timedivision multiple access (TDMA), in which the timeslot of each ONU isfixed beforehand and is not related to the actual traffic arrival rate.Without the overhead of the queue status report and the transmissiongrant, FBA is simple to implement. On the other hand, an ONU will occupythe upstream channel for its assigned timeslot even if there are no datato transmit, thus resulting in the increased delay for all the databuffered in other ONUs. A lot of data could be backlogged in the bufferswhile the upstream channel is lightly loaded or even idle, hence leadingto underutilization of the upstream channel.

Limited bandwidth allocation (LBA) monitors the incoming traffic byusing the MAC control messages to report the local queue size and tonotify the bandwidth arbitration decision (e.g., the REPORT/GATEmechanism in EPONs). The timeslot length of an ONU is upper-bounded bythe maximum timeslot length B_(max), which could be specified by SLA orother system parameters. When the reported queue size is less than thelimit, the OLT grants the bandwidth request; otherwise, B_(max) isgranted. LBA tracks the traffic load by means of the queue statusreports, the granted timeslot length varies according to the dynamictraffic, and the service cycle varies because ONUs are assigned withdifferent timeslot lengths in different service cycles. The conservativefeature of LBA confines each ONU by its own limit, thus restricting theaggressive competition for the upstream bandwidth.

Under the aforementioned bandwidth negotiation mechanism, each ONUexperiences a waiting time from sending the queue status to sending thebuffered data, as illustrated in FIG. 3. When sending a REPORT messageat time t₁, an ONU only reports the already buffered data to the OLT,and therefore, data arrived during the waiting time (i.e., t₃−t₁) haveto be deferred to the next timeslot even if the upstream channel islightly loaded. Credit-based bandwidth allocation (CBA) [3] takes suchdata into consideration, and when the OLT allocates the upstreambandwidth, it adds a credit into each ONU's requirement.B_(grant)=B_(queue)+C, where B_(grant), is the granted bandwidth to anONU, B_(queue) is the data (in terms of bandwidth) queued up in thebuffer, and C is the credit. C could be a constant credit or a linearcredit. The incoming data during the waiting time are expected to betransmitted (or partially transmitted) within the current timeslot.

In LBA, there might be some lightly loaded ONUs with the bandwidthrequirement less than the limits. The sum of the under-exploitedbandwidth of the lightly loaded ONUs is called the excessive bandwidth,i.e., B_(excess) As an extension of LBA, excessive bandwidthreallocation (EBR) exploits B_(excess), by redistributing it among theheavily loaded ONUs. The heavily loaded ONU_(k) obtains an additionalbandwidth, B_(add,k), where B_(add,k)=(B_(excess)*B_(max,k))/(Σ_(j)B_(max,i)), and B_(max,i) is the bandwidth limit of ONU_(i) specified inLBA.

Other than the upstream channel bandwidth allocation among differentONUs, it is necessary for a single ONU to provide multiple services toits different end users. Residing in customer premises, an ONU must becapable of supporting data, voice, and video services to the end users.This can be approached by means of a combination of queuing, scheduling,and class-based bandwidth allocation.

Different PON technologies provide their own support for servicedifferentiation. For example, in EPONs, as shown in FIG. 2, one 64-byteGATE message carries up to 6 grants to a particular ONU. The “number ofgrants” field specifies how many grants are in the message, and the“grant level” field indicates the order of the queues to which grantsare generated. Each grant contains a grant start time and a grantlength. One 64-byte REPORT message from an ONU reports up to 8 queues'status. The “report bitmap” field identifies the order of the reportedqueues. The OLT processes the queue status report, and sends back theGATE message including at least one grant, depending on the bandwidthallocation algorithm. Therefore, reporting multiple queues of an ONU andgranting multiple requirements to an ONU are possible, thus makingservice differentiation provisioning to the end users of an ONUfeasible.

Categorizing the traffic of an ONU into different classes is a practicalapproach for service differentiation [11]. The high priority class is“expedited forwarding” (EF), which is delay sensitive and requiresbandwidth guarantees. The medium priority class is “assured forwarding”(AF), which is not delay sensitive, but requires bandwidth guarantees.The low priority class is “best effort” (BE), which is neither delaysensitive nor bandwidth guaranteed. Data belonging to different classesare enqueued into their corresponding priority queues. All queues sharethe same buffer. When the buffer is full, the incoming data with ahigher priority replace the lower priority data while the incoming lowpriority data are dropped immediately. The buffered data are transmittedaccording to a specific scheduling scheme. As defined in IEEE 802.1D[12], the strict priority scheduling serves the buffered higher prioritydata first. The BE data can only be transmitted when the other twoqueues are empty. Adhering to the priority order, the strict priorityscheduling serves the higher priority data, which arrive during thewaiting time, ahead of the lower priority data, which may already bequeued up in the buffer. As shown in FIG. 3, the EF data arriving duringthe waiting time (i.e., t₇−t₅) will be served ahead of the AF and BEdata arriving earlier (i.e., before t₅). Therefore, the lower prioritydata suffer the uncontrolled increasing delay (if the buffer is notfull) or unfair drop (if the buffer is full).

The priority-based scheduling [5] tackles the unfairness by employingstrict priority scheduling within a specific time interval. After an ONUtransmits all buffered data in an interval, the data arriving after thisinterval will be served if the current timeslot can still transmit moredata. By configuring the interval as the time between sending the queuestatus (i.e., from t₁ to t₅ in FIG. 3), the higher priority dataarriving in the waiting time (i.e., t₇−t₅) will be served after allclasses of data of the previous interval (i.e., t₅−t₁) have been served.This scheme provides a bounded delay for the low priority data.

Reference [5] handles the class-based bandwidth allocation by collectingthe queue status from all ONUs before making decisions. The OLT assignsa fixed bandwidth to the EF traffic of all ONUs regardless of itsdynamics. The AF requests are granted as follows: if the sum of the AFrequests of all ONUs is less than or equal to the leftover bandwidthafter having served the EF services, all AF requests are granted;otherwise, the leftover bandwidth is equally distributed among all AFrequests. The leftover bandwidth after having served the EF and AFtraffic is distributed among all BE requests. The major drawbacksinclude the fixed bandwidth allocation for the EF traffic, whichpenalizes the AF and BE traffic by increasing the data delay; and thelong report collection time, which does not end until having receivedreports from all ONUs.

The algorithm proposed in reference [6] estimates the incoming EFtraffic in the waiting time by the amount of such data in the previouscycle, and is referred to as DBA2. The reported EF traffic is the sum ofthe buffered EF data plus the estimation, while the reported AF and BEtraffic is the actually buffered amount. The bandwidth requests aregranted by EBR, with the lightly loaded queues receiving instantaneousgrants while the grants for heavily loaded queues being deferred untilall reports have been received. DBA2 alleviates the delay of collectingall reports by granting the lightly loaded queues immediately. Thisalgorithm estimates the incoming EF data arriving during the waitingtime, and gives priority to the EF traffic by allocating the estimatedbandwidth. The drawback is that the service order of ONUs changes inevery service cycle, with the heavily loaded ONUs always being servedafter the lightly loaded ones, and therefore, the estimation of theincoming EF data is severely impaired because the waiting time of eachONU may change drastically.

Limited Sharing with Traffic Prediction (LSTP) Scheme

The LSTP scheme relies on the PON MAC control messages to allocate theupstream bandwidth. Each ONU predicts its bandwidth requirement of thenext timeslot, and sends a message to the OLT. The OLT determines thebandwidth allocation based on the report and the SLA.

Residing in customer premises, an ONU must be capable of supportingbroadband data, voice, and video services to the local users.Categorizing the traffic of an ONU into different classes is a practicalapproach for provisioning differentiated services. We borrow the trafficclasses from Diffserv [7], in which the high priority class is theexpedited forwarding (EF), the medium priority class is the assuredforwarding (AF), and the low priority class is the best effort (BE).Data belonging to different classes are enqueued into theircorresponding priority queues. All queues share the same buffer. Whenthe buffer is full, the incoming data with a higher priority replace thelower priority data while the incoming low priority data are droppedimmediately.

In LSTP, as shown in FIG. 4, each ONU transmits the buffered data to theOLT in its exclusively assigned timeslot. An ONU piggybacks itsbandwidth requirement of the next transmission by utilizing the upstreamcontrol message (e.g., the REPORT message of EPONs). The OLT grants therequirement by sending back a downstream message (e.g., the GATE messageof EPONs), and serves the ONUs in a fixed order (e.g., the OLT servesthe two ONUs alternately as shown in FIG. 4, which illustrates the EPONscenario).

The service interval of an ONU is defined as the time between its datatransmission. For example, as shown in FIG. 4, a service interval, sayn, with respect to ONU₁, ranges from time t₁ to time t₆. Time t₂ to timet₄ is the RTT between ONU₁ and the OLT plus the report processing time.Time t₂ to time t6 is the waiting time for ONU₁ in service interval n,during which ONU₁ is idle, and more data from the local users areenqueued. Service interval (n+1) of ONU₁ begins at time t₆, and thegranted timeslot from time t₆ to time t₈ is decided on the REPORTmessage sent at time t₂. With respect to ONU₂, service interval n beginsat time t₃ and ends at time t₉. Time t₃ to time t₅ is the exclusivetimeslot for ONU₂, and a report of its EF, AF, and BE queue status issent at time t₅. Time t₅ to time t₉ is the waiting time of ONU₂ inservice interval n.

In LSTP, an ONU predicts the data arrived during the waiting time interms of bandwidth as $\begin{matrix}{{{\overset{︵}{B}}_{i,c}^{w}\left( {n + 1} \right)} = {\sum\limits_{k = 0}^{L - 1}{{\alpha_{i,c,k}(n)}{B_{i,c}^{w}\left( {n - k} \right)}}}} & (1)\end{matrix}$where {circumflex over (B)}_(i,c) ^(w)(n+1) is the predicted data ofclass c (cε{EF, AF, BE}) at ONU_(i) arrived during the waiting time ofservice interval (n+1) in terms of bandwidth, B_(i,c) ^(w)(n) is theactual amount of class c data arrived at ONU_(i) during the waiting timeof service interval n in terms of bandwidth, α_(i,c,k)(n) is the weightfactor, and L is the order of the traffic predictor. The intuitionbehind the prediction is the network traffic self-similarity, whichimplies that network traffic exhibits a long-rang dependence [8], andtraffic is correlated between timeslots.

The weight factor is updated by the least mean squares (LMS) algorithm[9] as $\begin{matrix}{{{\alpha_{i,c,k}\left( {n + 1} \right)} = {{\alpha_{i,c,k}(n)} + {{\mu(n)} \cdot \frac{e_{i,c}(n)}{B_{i,c}^{w}(n)}}}},} & (2)\end{matrix}$where μ(n) is the step size, and e_(i,c)(n) is the prediction error,i.e.e _(i,c)(n)=B _(i,c) ^(w)(n)−{circumflex over (B)} _(i,c) ^(w)(n).  (3)

The computational complexity for the bandwidth prediction is O(L).

The predicted data in terms of bandwidth, i.e., {circumflex over(B)}_(i,c) ^(w)(n), if optimal, should be equal to the actual arriveddata in terms of bandwidth during the waiting time, i.e., B_(i,c)^(w)(n). Owing to the imperfection of the predictor, the predicted datamay turn out to be smaller or larger than the actual ones. Theprediction error in Eq. (3) is employed to adaptively adjust the stepsize, thus improving the prediction accuracy.

In service interval n, ONU_(i) requires its bandwidth for serviceinterval (n+1) by sending a message to the OLT, indicating the bandwidthrequirement for the next transmission. The bandwidth requirement is thesum of the enqueued data B_(i,c) ^(q)(n) and the prediction {circumflexover (B)}_(i,c) ^(w)(n+1), i.e.,B _(i,c) ^(r)(n+1)=B _(i,c) ^(q)(n)+{circumflex over (B)} _(i,c)^(w)(n+1).   (4)

The OLT instantaneously makes the bandwidth allocation decision afterhaving received a requirement. The granted bandwidth of class c trafficat ONU_(i) for service interval (n+1) isB _(i,c) ^(g)(n+1)=min{B _(i,c) ^(r)(n+1),S _(i,c)},  (5)where S_(i,c) is the maximum bandwidth parameter of ONU_(i) specified inthe SLA for traffic class c.

The bandwidth allocation of class c traffic at ONU_(i) is upper-boundedby the smaller value of the bandwidth request B_(i,c) ^(r)(n+1)_(i), andthe maximum bandwidth parameter S_(i,c). The assigned bandwidth, if thebandwidth requirement is no more than the maximum bandwidth parameter,dynamically changes upon the incoming traffic.

In the optimal case, when the actual incoming data are equal to theprediction result, all the enqueued data are transmitted from the ONU tothe OLT, and no data are deferred to the next timeslot. When the actualdata are less than the prediction, the assigned timeslot is long enoughfor the enqueued data, and the prediction is also deemed a success. Ifthe actual data exceed the prediction, the assigned timeslot can onlytransmit part of the enqueued data, and the leftover ones have to waitfor the next timeslot. The prediction fails in the last case. Theprediction success probability and its impact on the network performancewill be theoretically analyzed in the next section.

Performance Analysis

In this section we analyze the performance of LSTP in terms of thesuccess probability of bandwidth prediction, data loss, and data delay.For notational simplicity, we omit the referencing of the serviceinterval in the following analysis.

The prediction error plays a key factor on the network performance. Wecall the bandwidth prediction is successful if e_(i,c)=B_(i,c)^(w)−{circumflex over (B)}_(i,c) ^(w)≦0. Therefore, the successprobability of bandwidth prediction isP _(i,c) ^(s) =P{e _(i,c)≦0}.  (6)

For the LMS-based adaptive prediction, the prediction error is Gaussian[9]. Assume the prediction error has mean m_(i,c) and variance σ_(i,c)², i.e., e_(i,c)≈N(m_(i,c),σ_(i,c) ²), the success probability ofbandwidth prediction is $\begin{matrix}\begin{matrix}{P_{i,c}^{s} = {P\left\{ {e_{i,c} \leq 0} \right\}}} \\{= {\frac{1}{\sqrt{2\pi}\sigma_{i,c}}{\int_{- \infty}^{0}{{\mathbb{e}}^{{{- {({x - m_{i,c}})}^{2}}/2}\sigma_{i,c}^{2}}{\mathbb{d}x}}}}} \\{= {1 - {Q\left( {- \frac{m_{i,c}}{\sigma_{i,c}}} \right)}}} \\{{= {Q\left( \frac{m_{i,c}}{\sigma_{i,c}} \right)}},}\end{matrix} & (7)\end{matrix}$where Q(.) is the Q-function [10]. The probability that the predictionfails is P_(i,c) ^(f)=1−P_(i,c) ^(s).

The data delay is defined as the average time from enqueuing a packet atan ONU buffer to sending out the last bit of the packet to the OLT. Wefocus on the delay of the incoming data during the waiting time. InLSTP, the data delay differs according to the prediction result. Whenthe prediction succeeds, i.e., e_(i,c)=B_(i,c) ^(w)−{circumflex over(B)}_(i,c) ^(w)≦0, the required bandwidth is enough to transfer theincoming data during the waiting time to the OLT, and thus the datadelay is related to the average service interval length. Assume theaverage service interval length is t_(int), the data delay undersuccessful prediction is t_(int)/2. When e_(i,c)=B_(i,c)^(w)−{circumflex over (B)}_(i,c) ^(w)>0, the prediction fails, and suchincoming data have to wait for the next service interval fortransmission. The corresponding delay is the same as the one in a systemwithout traffic prediction, i.e., $\frac{t_{int}}{2} + {t_{int}.}$Combining both of the cases, the data delay is $\begin{matrix}\begin{matrix}{D = {{P_{i,c}^{s} \cdot \frac{t_{int}}{2}} + {P_{i,c}^{f} \cdot \left( {\frac{t_{int}}{2} + t_{int}} \right)}}} \\{= {\frac{3t_{int}}{2} - {P_{i,c}^{s} \cdot {t_{int}.}}}}\end{matrix} & (8)\end{matrix}$

Compared to a system without traffic prediction, LSTP improves the datadelay of the data arrived in the waiting time by $\begin{matrix}\begin{matrix}{\beta = \frac{D_{{no}\quad{prediction}} - D}{D_{{no}\quad{prediction}}}} \\{= \frac{P_{i,c}^{s} \cdot t_{int}}{\frac{3t_{int}}{2}}} \\{= {\frac{2}{3}{P_{i,c}^{s}.}}}\end{matrix} & (9)\end{matrix}$

The delay reduction is closely related to the prediction successprobability. More accurate prediction means a higher P_(i,c) ^(s), andthe delay of the data arrived during the waiting time will be furtherreduced.

LSTP employs the prioritized queuing mechanism. All classes of datashare a common physical buffer. The EF data have the highest priority,the AF data have the medium priority, and the BE data have the lowestpriority. The incoming higher priority data replace the lower priorityones if the buffer is full.

The EF traffic experiences data loss if the buffer is full and there areneither AF nor BE data already enqueued at the buffer. Assume the fixedbuffer size at ONU_(i) is A_(i), the EF fame loss probability at ONU_(i)is $\begin{matrix}\begin{matrix}{P_{i,{EF}}^{loss} = {P\left\{ {{B_{i,{EF}}^{w} + B_{i,{EF}}^{q}} > A_{i}} \right\}}} \\{= {P\left\{ {{B_{i,{EF}}^{w} - {\overset{︵}{B}}_{i,{EF}}^{w}} > {A_{i} - B_{i,{EF}}^{q} - {\overset{︵}{B}}_{i,{EF}}^{w}}} \right\}}} \\{= {P\left\{ {e_{i,{EF}} > {A_{i} - B_{i,{EF}}^{q} - {\overset{︵}{B}}_{i,{EF}}^{w}}} \right\}}} \\{{= {Q\left( \frac{A_{i} - B_{i,{EF}}^{q} - {\overset{︵}{B}}_{i,{EF}}^{w} - m_{i,{EF}}}{\sigma_{i,{EF}}} \right)}},}\end{matrix} & (10)\end{matrix}$where m_(i,EF) and σ_(i,EF) ² are the mean and variance of the EFtraffic prediction error e_(i,EF), respectively.

An incoming AF data is lost if the buffer is full and the enqueued databelong to either EF or AF traffics. The corresponding data lossprobability is $\begin{matrix}\begin{matrix}{P_{i,{AF}}^{loss} = {P\left\{ {{B_{i,{EF}}^{w} + B_{i,{EF}}^{q} + B_{i,{AF}}^{w} + B_{i,{AF}}^{q}} > A_{i}} \right\}}} \\{= {P{\left\{ {{e_{i,{EF}} + e_{i,{AF}}} > {A_{i} - B_{i,{EF}}^{q} - {\overset{︵}{B}}_{i,{EF}}^{w} - B_{i,{AF}}^{q} - {\overset{︵}{B}}_{i,{AF}}^{w}}} \right\}.}}}\end{matrix} & (11)\end{matrix}$

The EF and the AF traffics are independent, and LSTP employs dedicatedpredictors to these two traffics, respectively, and therefore, e_(i,EF)and e_(i,AF) are independent. Further assume thate_(i,EF)≈N(m_(i,EF),σ_(i,EF) ²) and e_(i,AF)≈N(m_(i,AF),σ_(i,AF) ²),then, $\begin{matrix}\begin{matrix}{P_{i,{AF}}^{loss} = {P\left\{ {{e_{i,{EF}} + e_{i,{AF}}} > {A_{i} - B_{i,{EF}}^{q} - {\overset{︵}{B}}_{i,{EF}}^{w} - B_{i,{AF}}^{q} - {\overset{︵}{B}}_{i,{AF}}^{w}}} \right\}}} \\{= {{Q\left( \frac{A_{i} - B_{i,{EF}}^{q} - {\overset{︵}{B}}_{i,{EF}}^{w} - B_{i,{AF}}^{q} - {\overset{︵}{B}}_{i,{AF}}^{w} - m_{i,{EF}} - m_{i,{AF}}}{\sqrt{\sigma_{i,{EF}}^{2} + \sigma_{i,{AF}}^{2}}} \right)}.}}\end{matrix} & (12)\end{matrix}$

Similarly, the BE data are lost if the buffer is full. Assume theprediction errors of the EF, AF, and BE traffic aree_(i,EF)≈N(m_(i,EF),σ_(i,EF) ²), e_(i,AF)≈N(m_(i,AF),σ_(i,AF) ²), and,re_(i,BB)≈N(m_(i,BB),σ_(i,BB) ²) respectively; as discussed above, theseprediction errors are independent. Therefore, the data loss probabilityof the BE traffic at ONU_(i) is $\begin{matrix}\begin{matrix}{P_{i,{BE}}^{loss} = {P\left\{ {{B_{i,{EF}}^{w} + B_{i,{EF}}^{q} + B_{i,{AF}}^{w} + B_{i,{AF}}^{q} + B_{i,{BE}}^{w} + B_{i,{BE}}^{q}} > A_{i}} \right\}}} \\{= {P\left\{ {{e_{i,\quad{EF}} + e_{i,\quad{AF}} + e_{i,\quad{BE}}} > {A_{i} - {\sum\limits_{c}\left( {B_{i,c}^{q} + {\overset{︵}{B}}_{i,c}^{w}} \right)}}} \right\}}} \\{= {{Q\left( \frac{A_{i} - {\sum\limits_{c}\left( {B_{i,c}^{q} + {\overset{︵}{B}}_{i,c}^{w} + m_{i,c}} \right)}}{\sqrt{\sum\limits_{c}\sigma_{i,c}^{2}}} \right)}.}}\end{matrix} & (13)\end{matrix}$Simulations

The LSTP scheme performance is evaluated via simulation results. Asystem model shown in FIG. 1 is set up in the OPNET simulator with oneOLT and 32 ONUs. Each ONU has a finite buffer of 20 Mbytes, and thedownstream and upstream channels are both 1 Gbps. The incoming trafficis self-similar with the Hurst parameter of 0.8. The length of Ethernetdata randomly varies from 64 bytes to 1518 bytes. The total traffic loadof the entire network is changing from 0.1 to 0.8; 20%, 30%, and 50% ofthe traffic are the EF, AF, and BE data, respectively. For comparisonpurposes, we applied the LBA scheme in reference [3], the DBA2 scheme inreference [6], and our proposed LSTP scheme on this system model. Theorder of the predictor in LSTP, i.e., L, is set to 4, and the step sizeμ is set by${\mu(n)} = {\frac{L}{\sum\limits_{k = 0}^{L - 1}\left\lbrack {B_{i,c}^{w}\left( {n - k} \right)} \right\rbrack^{2}}.}$

The figures of merits are the data delay and the data loss. FIG. 5illustrates the relationship between the average data delay and thenetwork traffic load. LBA experiences the longest delay, which isattributed to the fact that LBA disregards the incoming data during thewaiting time, and thus, more data are likely deferred to the nexttimeslot. DBA2 alleviates this problem by employing a rough predictionof the incoming EF traffic during the waiting time. The delay reductionfrom LBA to DBA2 shows that traffic prediction plays as a significantrole in reducing the upstream transmission latency. LSTP outperformsboth DBA2 and LBA. Several points contribute to the shortest averagedata delay in LSTP. First, LSTP predicts all classes of traffic insteadof only one class in DBA2 and no traffic prediction in LBA. Second, theprediction accuracy has been improved in LSTP by employing the LMS-basedpredictor, which is suitable for adaptive on-line traffic prediction.Third, LSTP implements the fixed ONU service order instead of thedynamic service order in DBA2, and reduces the drastic change of theservice interval length in DBA2, thus facilitating the trafficprediction. Forth, the OLT replies the ONU bandwidth requirementinstantaneously in LSTP. In DBA2, the heavily-loaded ONUs are alwaysserved after the lightly-loaded ones, and the deferred service for thoseheavily-loaded ONUs results in longer delay of the incoming data.

A shorter average data delay means that the ONUs transmit the datafaster, and therefore, less chance that data are dropped because ofbuffer overflow. The performance of LBA, DBA2, and LSTP in terms of thedata loss exhibits the similar trend to that of the data delay. The dataloss ratio is defined as the number of dropped data versus the totalnumber of data. Again, as shown in FIG. 6, LBA has the most data loss,and LSTP has the least, implying that the traffic prediction andinstantaneous bandwidth allocation provided by LSTP alleviate the dataloss by requesting the predicted bandwidth, thus reducing the number ofbacklogged data at the buffer.

The disclosed LSTP scheme enhances the upstream bandwidth sharing amongONUs by means of the class-based traffic prediction and the SLA-basedupper-bounded bandwidth allocation. The performance of LSTP has beentheoretically analyzed in terms of the prediction success probability,the average data delay, and the class-based data loss probability. Thesimulation results demonstrate that LSTP enhances the accuracy of theprediction of the incoming data during the waiting time, and theimproved traffic prediction thus contributes to the reduction of datalatency and loss.

Although preferred embodiments of the invention have been disclosed forillustrative purposes, those skilled in the art will appreciate thatmany additions, modifications and substitutions are possible withoutdeparting from the scope and spirit of the invention as defined by theaccompanying claims.

REFRENCES

-   [1] IEEE 802.3ah task force home page. http://www.ieee802.org/3/efm-   [2] ITU-T Study Group 15 home page.    http://www.itu.int/ITU-T/studygroups/com15/index.asp-   [3] G. Kramer, B. Mukherjee, and G. Pesavento, “IPACT: a dynamic    protocol for an Ethernet PON (EPON),” IEEE Communications Magazine,    vol. 40, no. 2, pp. 74-80, February 2002.-   [4] M. Ma, Y. Zhu, and T. H. Cheng, “A bandwidth guaranteed polling    MAC protocol for Ethernet passive optical networks,” in Proc. IEEE    INFOCOM, San Francisco, Calif., pp. 22-31, March-April 2003.-   [5] S. Choi and J. Huh, “Dynamic bandwidth allocation algorithm for    multimedia services over Ethernet PONs,” ETRI Journal, vol. 24, no.    6, pp. 465-468, December 2002.-   [6] C. M. Assi, Y. Ye, D. Sudhir, and M. A. Ali, “Dynamic bandwidth    allocation for quality-of-service over Ethernet PONs,” IEEE Journal    on Selected Areas in Communications, vol. 21, no. 9, pp. 1467-1477,    November 2003.-   [7] S. Blake, D. Black, M. Carlson, E. Davies, Z. Wang, and W.    Weiss, “An architecture for differentiated services,” IETF RFC 2475.-   [8] W. Willinger, M. Taqqu, R. Sherman, and D. Wilson,    “Self-similarity through high-variability: statistical analysis of    Ethernet LAN traffic at the source level,” IEEE/ACM Transactions on    Networking, vol. 5, no. 1, pp. 71-86, February 1997.-   [9] S. Haykin, Adaptive filter theory, 3^(rd) edition, Prentice    Hall, 1996.-   [10] A. Leon-Garcia, Probability and random processes for electrical    engineering, 2^(nd) edition, Addison-Wesley, 1993.-   [11] G. Kramer and G. Pesavento, “Ethernet passive optical network    (EPON): building a next-generation optical access network,” IEEE    Commun. Mag., vol. 40, no. 2, February 2002, pp. 66-73.-   [12] IETF RFC 2475, “An architecture for differentiated services,”    December 1998.-   [13] ANSI/IEEE Standard 802.1D, part 3: Media Access Control (MAC)    Bridges, 1998.

1. In a communication network including a terminal which serves aplurality of downstream network units and receives upstreamcommunications from the network units in respective time slots on a timedivision basis, the terminal apportioning upstream bandwidth amongnetwork units by granting a terminal-determined variable duration to therespective timeslots of network units in response to a request from anetwork unit, a network unit storing local information received therebyand destined for the terminal, a method for allocating a timeslot to anetwork unit, a method for providing network bandwidth allocation to thenetwork terminals comprising causing the unit's requested duration toinclude the transmission time of stored local data awaiting transmissionfrom the network unit to the terminal as well as a waiting time which isan estimate of the time required to transmit from the unit to theterminal that data which is expected to be received in the intervalbetween the request and the initiation of transmission from the unit tothe terminal after receiving the grant.
 2. The method of claim 1performed in a network wherein the terminal is an optical line terminallocated at a service provider's central office and the network units areoptical network units, optically linked to the terminal.
 3. The methodof claim 2 wherein the terminal is a passive splitter for downstreamcommunication.
 4. The method of claim 1 wherein a request is sent by theunit at the conclusion of sending a burst of data, the estimate of timebeing the time required to transmit data received in the intervalbetween the request and the next following initiation of transmissionfrom the unit to the terminal.
 5. The method of claim 1 performed in anetwork wherein sets of information are stored by a network unit, eachmember of a set of local information having one of a plurality ofpredefined priority levels, the method further comprising, at a networkunit, maintaining a buffer of members to be transmitted to the terminal,enqueing members for transmission to the terminal in queues eachdedicated to members of a specific priority, and in the interval betweena network unit request and a following transmission of data thereby,replacing data in the buffer with data being received which is of ahigher priority when the buffer is full.
 6. The method of claim 5performed in a network wherein the terminal is an optical line terminallocated at a service provider's central office and the network units areoptical network units, optically linked to the terminal.
 7. The methodof claim 6 wherein the terminal is a passive splitter for downstreamcommunication.
 8. The method of claim 5 wherein a request is sent by theunit at the conclusion of sending a burst of data, the estimate of timebeing the time required to transmit data received in the intervalbetween the request and the next following initiation of transmissionfrom the unit to the terminal.
 9. The method of claim 5 wherein thereare three priorities defined, in descending order of priority, as toppriority for members which are delay sensitive and have bandwidthguarantees, second priority for members which are not delay sensitivebut have bandwidth guarantees, and low priority for members which arenot delay sensitive and do not have bandwidth guarantees.
 10. In acommunication network including a terminal which serves a plurality ofdownstream network units and receives upstream communications from thenetwork units in respective time slots on a time division basis, theterminal apportioning upstream timeslots among network units by grantinga respective timeslot of to network units in response to a request froma network unit, a network unit storing sets of local informationreceived thereby and destined for the terminal, each member of a set oflocal information having one of a plurality of predefined prioritylevels, a method providing service differentiation among informationsets of different priority comprising, at a network unit, maintaining abuffer of members to be transmitted to the terminal, enqueing membersfor transmission to the terminal in queues each dedicated to members ofa specific priority, in the interval between a network unit request anda following transmission of data thereby, replacing data in the bufferwith data being received which is of a higher priority when the bufferis full.
 11. The method of claim 10 wherein there are three prioritiesdefined, in descending order of priority, as top priority for memberswhich are delay sensitive and have bandwidth guarantees, second priorityfor members which are not delay sensitive but have bandwidth guarantees,and third priority for members which are not delay sensitive and do nothave bandwidth guarantees
 12. In a communication network including aterminal which serves a plurality of downstream network units andreceives upstream communications from the network units in respectivetime slots on a time division basis, the terminal apportioning upstreambandwidth among network units by granting a terminal-determined variableduration to the respective timeslots of network units in response to arequest from a network unit, a network unit storing local informationreceived thereby and destined for the terminal, a method for allocatinga timeslot to a network unit, the improvement comprising a network unitbeing constructed so that its requested duration includes thetransmission time of stored local data awaiting transmission from thenetwork unit to the terminal as well as a waiting time which is anestimate of the time required to transmit from the unit to the terminalthat data which is expected to be received in the interval between therequest and the initiation of transmission from the unit to the terminalafter receiving the grant.
 13. The improved network unit of claim 12wherein the terminal is an optical line terminal located at a serviceprovider's central office and the network units are optical networkunits, optically linked to the terminal.
 14. The improved network unitof claim 13 constructed to interface with a terminal which is a passivesplitter for downstream communication.
 15. The improved network unit ofclaim 12 wherein the unit is constructed to send a request at theconclusion of sending a burst of data, the estimate of time being thetime required to transmit data received in the interval between therequest and the next following initiation of transmission from the unitto the terminal.
 16. The improved network unit of claim 12 wherein setsof information are stored by the network unit, each member of a set oflocal information having one of a plurality of predefined prioritylevels, the network unit further comprising a buffer of members to betransmitted to the terminal, a plurality of queues for members fortransmission to the terminal in each queues dedicated to members of aspecific priority, the network unit being constructed to replace data inthe buffer with data being received in the interval between a networkunit request and a following transmission of data thereby which is of ahigher priority when the buffer is full.
 17. The improved network unitof claim 12 wherein the network unit is an optical network unit andcommunicates with a terminal which is an optical line terminal locatedat a service provider's central office and optically linked to theterminal.
 18. The improved network unit of claim 17 wherein the networkunit id constructed to interface with a terminal that is a passivesplitter for downstream communication.
 19. The improved network unit ofclaim 16 wherein the unit is constructed to send a request at theconclusion of sending a burst of data, the estimate of time being thetime required to transmit data received in the interval between therequest and the next following initiation of transmission from the unitto the terminal.
 20. The improved network unit of claim 16 wherein thereare three priorities defined, in descending order of priority, as toppriority for members which are delay sensitive and have bandwidthguarantees, second priority for members which are not delay sensitivebut have bandwidth guarantees, and low priority for members which arenot delay sensitive and do not have bandwidth guarantees.
 21. In acommunication network including a terminal which serves a plurality ofdownstream network units and receives upstream communications from thenetwork units in respective time slots on a time division basis, theterminal apportioning upstream timeslots among network units by grantinga respective timeslot of to network units in response to a request froma network unit, a network unit storing sets of local informationreceived thereby and destined for the terminal, each member of a set oflocal information having one of a plurality of predefined prioritylevels, an improved network unit providing service differentiation amonginformation sets of different priority comprising, a buffer of membersto be transmitted to the terminal, a plurality of queues for members fortransmission to the terminal, each queue dedicated to members of aspecific priority, a network unit being constructed to replace data inthe buffer with data received in the interval between a network unitrequest and a following transmission of data thereby which is of ahigher priority when the buffer is full,.
 22. The improved network unitof claim 21 constructed to have three priorities defined, in descendingorder of priority, as top priority for members which are delay sensitiveand have bandwidth guarantees, second priority for members which are notdelay sensitive but have bandwidth guarantees, and third priority formembers which are not delay sensitive and do not have bandwidthguarantees.